




import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType







class 				lowerCAmelCase_       :






				def __init__(			self      ,snake_case__=2      ,snake_case__=3      ,snake_case__=64      ,snake_case__=None ):
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      np.random.default_rng(snake_case__ )
							SCREAMING_SNAKE_CASE_   : int					  =      length
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      rng.normal(size=(length,) ).astype(np.floataa )
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      a * self.x + b + rng.normal(scale=0.1      ,size=(length,) ).astype(np.floataa )






				def __len__(			self ):
							return self.length






				def __getitem__(			self      ,snake_case__ ):
							return {"x": self.x[i], "y": self.y[i]}







class 				lowerCAmelCase_       (					torch.nn.Module							):






				def __init__(			self      ,snake_case__=0      ,snake_case__=0      ,snake_case__=False ):
							super().__init__()
							SCREAMING_SNAKE_CASE_   : Dict					  =      torch.nn.Parameter(torch.tensor([2, 3] ).float() )
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      torch.nn.Parameter(torch.tensor([2, 3] ).float() )
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      True






				def 		snake_case	(			self      ,snake_case__=None ):
							if self.first_batch:
										print(F'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}' )
										SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      False
							return x * self.a[0] + self.b[0]







class 				lowerCAmelCase_       (					torch.nn.Module							):






				def __init__(			self      ,snake_case__=0      ,snake_case__=0      ,snake_case__=False ):
							super().__init__()
							SCREAMING_SNAKE_CASE_   : List[str]					  =      torch.nn.Parameter(torch.tensor(snake_case__ ).float() )
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      torch.nn.Parameter(torch.tensor(snake_case__ ).float() )
							SCREAMING_SNAKE_CASE_   : Tuple					  =      True






				def 		snake_case	(			self      ,snake_case__=None ):
							if self.first_batch:
										print(F'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}' )
										SCREAMING_SNAKE_CASE_   : Tuple					  =      False
							return x * self.a + self.b





def        __UpperCAmelCase						(							lowerCamelCase_ :     Optional[Any]  ,							lowerCamelCase_ :     int = 16  )       ->  List[str]:







			"""simple docstring"""





			from datasets import load_dataset
			from transformers import AutoTokenizer

			SCREAMING_SNAKE_CASE_   : Optional[int]					  =      AutoTokenizer.from_pretrained('bert-base-cased'  )
			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      {'train': 'tests/test_samples/MRPC/train.csv', 'validation': 'tests/test_samples/MRPC/dev.csv'}
			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      load_dataset('csv'  ,							data_files=lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : Dict					  =      datasets['train'].unique('label'  )

			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      {v: i for i, v in enumerate(lowerCamelCase_  )}

			def tokenize_function(lowerCamelCase_ :     List[str]  ):
						# max_length=None => use the model max length (it's actually the default)
						SCREAMING_SNAKE_CASE_   : Dict					  =      tokenizer(
						    examples['sentence1']  ,							examples['sentence2']  ,							truncation=lowerCamelCase_  ,							max_length=lowerCamelCase_  ,							padding='max_length'  )
						if "label" in examples:
									SCREAMING_SNAKE_CASE_   : str					  =      [label_to_id[l] for l in examples['label']]
						return outputs

			# Apply the method we just defined to all the examples in all the splits of the dataset
			SCREAMING_SNAKE_CASE_   : Optional[int]					  =      datasets.map(
			    lowerCamelCase_  ,							batched=lowerCamelCase_  ,							remove_columns=['sentence1', 'sentence2', 'label']  ,							)

			def collate_fn(lowerCamelCase_ :     int  ):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						if accelerator.distributed_type == DistributedType.TPU:
									return tokenizer.pad(lowerCamelCase_  ,							padding='max_length'  ,							max_length=1_28  ,							return_tensors='pt'  )
						return tokenizer.pad(lowerCamelCase_  ,							padding='longest'  ,							return_tensors='pt'  )

			# Instantiate dataloaders.
			SCREAMING_SNAKE_CASE_   : Any					  =      DataLoader(tokenized_datasets['train']  ,							shuffle=lowerCamelCase_  ,							collate_fn=lowerCamelCase_  ,							batch_size=2  )
			SCREAMING_SNAKE_CASE_   : Optional[int]					  =      DataLoader(tokenized_datasets['validation']  ,							shuffle=lowerCamelCase_  ,							collate_fn=lowerCamelCase_  ,							batch_size=1  )

			return train_dataloader, eval_dataloader




def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )		->	list:







 '''simple docstring'''
 UpperCamelCase			  =      len(UpperCamelCase_       )
 UpperCamelCase			  =      [[0] * n for i in range(UpperCamelCase_       )]
 for i in range(UpperCamelCase_       ):
  UpperCamelCase			  =      y_points[i]

 for i in range(2							,					UpperCamelCase_       ):
  for j in range(UpperCamelCase_							,					UpperCamelCase_       ):
   UpperCamelCase			  =      (
       (xa - x_points[j - i + 1]) * q[j][i - 1]
       - (xa - x_points[j]) * q[j - 1][i - 1]
   ) / (x_points[j] - x_points[j - i + 1])

 return [q[n - 1][n - 1], q]


if __name__ == "__main__":
     import doctest

     doctest.testmod()
